Non-destructively Determining Blueberry (Vaccinium corymbosum L.) Leaf Area Using DPI-Based Software

Year 2022, Volume: 5 Issue: 3, 195 - 199, 01.07.2022

Mehmet Serhat Odabas , Gökhan Kayhan , Hüseyin Çelik , Recai Oktaş

https://doi.org/10.47115/bsagriculture.1090017

Cited By: 1

Abstract

Blueberries (Vaccinium spp.) are a popular crop all throughout the world. Blueberry (Vaccinium corymbosum L.) leaves were randomly selected from the experimental area of Ondokuz Mayis University, Faculty of Agriculture as a research material. Blueberries are high in polyphenolic chemicals, particularly anthocyanins, which are antioxidants and anti-inflammatory. A total of 1500 leaves were collected, with 100 for each cultivar, to represent the variety of sizes found on 15 plants of each cultivar. Manual leaf area measurement was made with a digital planimeter. The area was measured using a 300dpi resolution image read from the relevant file in such a way that every one of them is defined by 8bits in RGB color space. A weighted sum of the RGB components of the image is used to convert RGB values to grayscale values. This three-dimensional gray image generates a binary image. The Otsu method was used to determine the threshold value required to minimize the in-class variation of the threshold black and white pixels. Simulink was used for easy use of the end-user with the developed software. This software can be used for the area measurement of all plant leaves.

Keywords

Blueberry, Leaf area, Software, Vaccinium corymbosum L.

References

• Anonymous. 2022. URL: https://wmaraci.com/nedir/gui (access date: March 18, 2022).
• Baar S, Kobayashi Y, Horie T, Sato K, Suto K, Watanabe S. 2022. Non-destructive leaf area index estimation via guided optical imaging for large scale greenhouse environments. Comp Elect Agri, 197: 106911.
• Campostrini E, Yamanishi OK. 2001. Estimation of papaya leaf area using the central vein length, Scientia Agric, 58: 39-42.
• Cristofori V, Rouphael Y, Gyves EM, Bignami C. 2007. A simple model for estimating leaf area of hazelnut from linear measurements. Scientia Hort, 113: 221-225.
• Çelik H, Odabas MS, Odabas F. 2011. Leaf area prediction models for highbush blueberries (Vaccinium corymbosum L.) from linear measurement. Adv Food Sci, 33(1): 16-21.
• Çelik H, Uzun S. 2002. Validation of leaf area estimation models (uzcelik-1) evaluated for some horticultural plants. Pakistan J Bot, 34: 41-46.
• Giurida F, Rouphael Y, Toscano S, Scuderi D, Romano D, Rivera C, Colla G, Leonardi C. 2011. Simple model for nondestructive leaf area estimation in bedding plants. Photosynthetica, 49: 380-388.
• Goncalves CAA, Chalfin NNJ, Regina MA, Alvarenga AA, Souza MT, Abraao E. 2002. Estimativa da área foliar da videira (Vitis labrusca L. cv. Folha de Figo) sobre diferentes porta-enxertos. Ciência e Agrotec, 26: 500-504.
• Kersteins G, Hawes CW. 1994. Response of growth and carbon allocation to elevated CO2 in young cherry (Prunus avium L) saplings in relation to root environment. New Phytol, 128: 607-614.
• Kliewer WM, Dokoozlian NK. 2005. Leaf area/crop weight ratios of grapevines: Influence on fruit composition and wine quality. Am J Enol Vitic, 56: 170-181.
• Montero FJ, Deuan JA, Cuesta A, Brasa A. 2000. Nondestructive methods to estimate leaf area in Vitis vinifera L. Hort Sci, 35: 696-698.
• Mora M, Avila F, Carrasco-Benavides M, Maldonado G, Olguín-Cáceres J, Fuentes S. 2016. Automated computation of leaf area index from fruit trees using improved image processing algorithms applied to canopy cover digital photograpies. Comp Elect Agri, 123: 195-202.
• Nesmith DS. 1991. Non-destructive leaf area estimation of rabbiteye blueberries. Hort Sci, 26: 13-32.
• Odabas MS, Çelik H, Islam A. 2009. Non-destructive leaf area prediction model for “Kiraz” chery-laurel (Laurocerasus officinalis Roem.). European J Plant Sci Biotechnol, 3(special issue 1): 97-99.
• Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transact Syst, Man, and Cybernet, 9(1): 62–66.
• Pandey SK, Singh H. 2011. A simple, cost-effective method for leaf area estimation. J Botany, 2011: 658240. doi: 10.1155/2011/658240.
• Potdar MV, Pawar KR. 1991. Non-destructive leaf area estimation in banana. Scientia Hort, 45: 251-254.
• Rao GSP, Khna BH, Chadha KL. 1978. Comparison of methods of estimating leaf-surface area through leaf characteristics in some cultivars of Mangifera indica. Scientia Hort, 8: 341-348.
• Retamales JB, Hancock JF. 2018. Blueberries, 2nd Edition. Crop Prod. Sci. in Horticulture Series, 29, CABI, pp: 425. doi: 10.1079/9781780647265.0000.
• Shabani A, Ghaffary KA, Sepaskhah AR, Kamgar-Haghighi AA. 2017. Using the artificial neural network to estimate leaf area. Scientia Hort, 216: 103-110.
• Shakya R, Lal MA. 2018. Photoassimilate translocation. In plant physiology, development and metabolism. Springer, Berlin, Germany, pp: 227-251.
• Turgeon R. 1989. The sink-source transition in leaves. Annu Rev Plant Biol, 40: 119-138.
• Uzun S, Celik H. 1999. Leaf area prediction models (uzcelik-1) for different horticultural plants. Tr J Agric Forest, 23: 645-650.
• Yin T, Cook BD, Morton DC. 2022. Three-dimensional estimation of deciduous forest canopy structure and leaf area using multi-directional, leaf-on and leaf-off airborne lidar data. Agri Forest Meteorol, 314: 108781.